#!/usr/bin/env python3
# ==========================================================
# FILE: ghostpack.py
# ==========================================================
import os, sys, time, json, argparse, importlib.util, subprocess, traceback
import torch, einops, numpy as np, gradio as gr
from PIL import Image
from diffusers import AutoencoderKLHunyuanVideo
from transformers import (
    LlamaModel, CLIPTextModel, LlamaTokenizerFast, CLIPTokenizer,
    SiglipImageProcessor, SiglipVisionModel
)
try:
    from diffusers_helper.hf_login import login
    from diffusers_helper.hunyuan import (
        encode_prompt_conds, vae_decode, vae_encode, vae_decode_fake
    )
    from diffusers_helper.utils import (
        save_bcthw_as_mp4, crop_or_pad_yield_mask, soft_append_bcthw,
        resize_and_center_crop, generate_timestamp
    )
    from diffusers_helper.models.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked
    from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan
    from diffusers_helper.memory import (
        gpu, get_cuda_free_memory_gb, move_model_to_device_with_memory_preservation,
        offload_model_from_device_for_memory_preservation, fake_diffusers_current_device,
        DynamicSwapInstaller, unload_complete_models, load_model_as_complete
    )
    from diffusers_helper.thread_utils import AsyncStream, async_run
    from diffusers_helper.gradio.progress_bar import make_progress_bar_css, make_progress_bar_html
    from diffusers_helper.clip_vision import hf_clip_vision_encode
    from diffusers_helper.bucket_tools import find_nearest_bucket
except ImportError as e:
    with open(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'outputs', 'install_logs.txt'), 'a') as f:
        f.write(f"[Dependency Error] {str(e)}\n")
    print(f"Dependency error: {str(e)}. Check outputs/install_logs.txt.")
    sys.exit(1)

try:
    from huggingface_hub import hf_hub_download
    from safetensors.torch import load_file
except ImportError as e:
    with open(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'outputs', 'install_logs.txt'), 'a') as f:
        f.write(f"[Dependency Error] {str(e)}\n")
    print(f"Dependency error: {str(e)}. Install huggingface_hub and safetensors: pip install huggingface_hub safetensors")
    sys.exit(1)

# ------------------------- CLI ----------------------------
parser = argparse.ArgumentParser()
parser.add_argument('--share', action='store_true')
parser.add_argument('--server', type=str, default='0.0.0.0')
parser.add_argument('--port', type=int)
parser.add_argument('--inbrowser', action='store_true')
parser.add_argument('--cli', action='store_true')
args = parser.parse_args()

BASE = os.path.abspath(os.path.dirname(__file__))
os.environ['HF_HOME'] = os.path.join(BASE, 'hf_download')
LORA_CACHE = os.path.join(BASE, 'dlora')
os.makedirs(LORA_CACHE, exist_ok=True)

# Set HF token from environment variable
HF_TOKEN = os.getenv('HF_TOKEN', 'XXXXXXXXXXXXXXXXXXXXXXXX')

if args.cli:
    print("👻 GhostPack F1 Pro CLI\n")
    print("python ghostpack.py           # launch UI")
    print("python ghostpack.py --cli     # show help\n")
    sys.exit(0)

# ---------------------- Paths -----------------------------
OUT_BASE    = os.path.join(BASE, 'outputs')
OUT_IMG     = os.path.join(OUT_BASE, 'img')
OUT_TMP     = os.path.join(OUT_BASE, 'tmp_vid')
OUT_VID     = os.path.join(OUT_BASE, 'vid')
PROMPT_LOG  = os.path.join(OUT_BASE, 'prompts.txt')
SAVED_PROMPTS = os.path.join(OUT_BASE, 'saved_prompts.json')
INSTALL_LOG = os.path.join(OUT_BASE, 'install_logs.txt')
for d in (OUT_BASE, OUT_IMG, OUT_TMP, OUT_VID):
    os.makedirs(d, exist_ok=True)
if not os.path.exists(SAVED_PROMPTS):
    json.dump([], open(SAVED_PROMPTS,'w'))
if not os.path.exists(INSTALL_LOG):
    open(INSTALL_LOG,'w').close()

# ---------------- Auto-Downloader ------------------------
def auto_download_fastvideo_lora():
    repo_id = "Kijai/HunyuanVideo_comfy"
    filename = "hyvideo_FastVideo_LoRA-fp8.safetensors"
    try:
        msg, lora_path = download_lora(repo_id, filename, HF_TOKEN)
        return msg
    except Exception as e:
        with open(INSTALL_LOG, 'a') as f:
            f.write(f"[Auto-Download Error] {repo_id}/{filename}: {str(e)}\n")
        return f"❌ Auto-download failed: {str(e)}"

# Run auto-downloader at startup
auto_download_status = auto_download_fastvideo_lora()

# ---------------- Prompt utils ---------------------------
def get_last_prompts():
    return json.load(open(SAVED_PROMPTS))[-5:][::-1]

def save_prompt_fn(p, n):
    if not p:
        return "❌ No prompt"
    data = json.load(open(SAVED_PROMPTS))
    entry = {'prompt': p, 'negative': n}
    if entry not in data:
        data.append(entry)
        json.dump(data, open(SAVED_PROMPTS,'w'))
    return "✅ Saved"

def load_prompt_fn(idx):
    lst = get_last_prompts()
    return lst[idx]['prompt'] if idx < len(lst) else ""

# ---------------- Cleanup utils --------------------------
def clear_temp_videos():
    try:
        [os.remove(os.path.join(OUT_TMP,f)) for f in os.listdir(OUT_TMP)]
        return "✅ Temp cleared"
    except Exception as e:
        return f"❌ Failed to clear temp: {str(e)}"

def clear_old_files():
    try:
        cutoff = time.time() - 7*24*3600
        c = 0
        for d in (OUT_TMP, OUT_IMG):
            for f in os.listdir(d):
                p = os.path.join(d, f)
                if os.path.isfile(p) and os.path.getmtime(p) < cutoff:
                    os.remove(p)
                    c += 1
        return f"✅ {c} old files removed"
    except Exception as e:
        return f"❌ Failed to clear old files: {str(e)}"

def clear_images():
    try:
        [os.remove(os.path.join(OUT_IMG,f)) for f in os.listdir(OUT_IMG)]
        return "✅ Images cleared"
    except Exception as e:
        return f"❌ Failed to clear images: {str(e)}"

def clear_videos():
    try:
        [os.remove(os.path.join(OUT_VID,f)) for f in os.listdir(OUT_VID)]
        return "✅ Videos cleared"
    except Exception as e:
        return f"❌ Failed to clear videos: {str(e)}"

# ---------------- Gallery helpers ------------------------
def list_images():
    try:
        return sorted(
            [os.path.join(OUT_IMG,f) for f in os.listdir(OUT_IMG) if f.lower().endswith(('.png','.jpg'))],
            key=os.path.getmtime
        )
    except Exception:
        return []

def list_videos():
    try:
        return sorted(
            [os.path.join(OUT_VID,f) for f in os.listdir(OUT_VID) if f.lower().endswith('.mp4')],
            key=os.path.getmtime
        )
    except Exception:
        return []

def list_loras():
    try:
        return sorted(
            [os.path.join(LORA_CACHE,f) for f in os.listdir(LORA_CACHE) if f.lower().endswith('.safetensors')],
            key=os.path.getmtime
        )
    except Exception:
        return []

def load_image(sel):
    try:
        imgs = list_images()
        if sel in [os.path.basename(p) for p in imgs]:
            pth = imgs[[os.path.basename(p) for p in imgs].index(sel)]
            return gr.update(value=pth), gr.update(value=os.path.basename(pth))
        return gr.update(), gr.update()
    except Exception as e:
        return gr.update(), gr.update(value=f"❌ Error: {str(e)}")

def load_video(sel):
    try:
        vids = list_videos()
        if sel in [os.path.basename(p) for p in vids]:
            pth = vids[[os.path.basename(p) for p in vids].index(sel)]
            return gr.update(value=pth), gr.update(value=os.path.basename(pth))
        return gr.update(), gr.update()
    except Exception as e:
        return gr.update(), gr.update(value=f"❌ Error: {str(e)}")

def load_lora_select(sel):
    try:
        loras = list_loras()
        if sel in [os.path.basename(p) for p in loras]:
            pth = loras[[os.path.basename(p) for p in loras].index(sel)]
            return gr.update(value=pth), gr.update(value=os.path.basename(pth))
        return gr.update(), gr.update()
    except Exception as e:
        return gr.update(), gr.update(value=f"❌ Error: {str(e)}")

def next_image_and_load(sel):
    try:
        imgs = list_images()
        if not imgs:
            return gr.update(), gr.update()
        names = [os.path.basename(i) for i in imgs]
        idx = (names.index(sel)+1) % len(names) if sel in names else 0
        pth = imgs[idx]
        return gr.update(value=pth), gr.update(value=os.path.basename(pth))
    except Exception:
        return gr.update(), gr.update()

def next_video_and_load(sel):
    try:
        vids = list_videos()
        if not vids:
            return gr.update(), gr.update()
        names = [os.path.basename(v) for v in vids]
        idx = (names.index(sel)+1) % len(names) if sel in names else 0
        pth = vids[idx]
        return gr.update(value=pth), gr.update(value=os.path.basename(pth))
    except Exception:
        return gr.update(), gr.update()

def next_lora_and_load(sel):
    try:
        loras = list_loras()
        if not loras:
            return gr.update(), gr.update()
        names = [os.path.basename(l) for l in loras]
        idx = (names.index(sel)+1) % len(names) if sel in names else 0
        pth = loras[idx]
        return gr.update(value=pth), gr.update(value=os.path.basename(pth))
    except Exception:
        return gr.update(), gr.update()

def gallery_image_select(evt: gr.SelectData):
    try:
        imgs = list_images()
        if evt.index is not None and evt.index < len(imgs):
            pth = imgs[evt.index]
            return gr.update(value=pth), gr.update(value=os.path.basename(pth))
        return gr.update(), gr.update()
    except Exception:
        return gr.update(), gr.update()

def gallery_video_select(evt: gr.SelectData):
    try:
        vids = list_videos()
        if evt.index is not None and evt.index < len(vids):
            pth = vids[evt.index]
            return gr.update(value=pth), gr.update(value=os.path.basename(pth))
        return gr.update(), gr.update()
    except Exception:
        return gr.update(), gr.update()

def gallery_lora_select(evt: gr.SelectData):
    try:
        loras = list_loras()
        if evt.index is not None and evt.index < len(loras):
            pth = loras[evt.index]
            return gr.update(value=pth), gr.update(value=os.path.basename(pth))
        return gr.update(), gr.update()
    except Exception:
        return gr.update(), gr.update()

# ---------------- Install status -------------------------
def check_mod(n):
    return importlib.util.find_spec(n) is not None

def status_xformers():
    return "✅ xformers" if check_mod("xformers") else "❌ xformers"

def status_sage():
    return "✅ sage-attn" if check_mod("sageattention") else "❌ sage-attn"

def status_flash():
    return "✅ flash-attn" if check_mod("flash_attn") else "⚠️ flash-attn"

def install_pkg(pkg, warn=None):
    try:
        if warn:
            print(warn)
            time.sleep(1)
        out = subprocess.check_output(
            [sys.executable, "-m", "pip", "install", pkg],
            stderr=subprocess.STDOUT, text=True
        )
        res = f"✅ {pkg}\n{out}\n"
    except subprocess.CalledProcessError as e:
        res = f"❌ {pkg}\n{e.output}\n"
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[{pkg}] {res}")
    return res

install_xformers    = lambda: install_pkg("xformers")
install_sage_attn   = lambda: install_pkg("sage-attn")
install_flash_attn  = lambda: install_pkg("flash-attn","⚠️ long compile")
refresh_logs        = lambda: open(INSTALL_LOG).read()
clear_logs          = lambda: (open(INSTALL_LOG,'w').close() or "✅ Logs cleared")

# ---------------- LoRA Download and Load ------------------
def download_lora(repo_id, filename, hf_token):
    try:
        lora_path = os.path.join(LORA_CACHE, filename)
        if not os.path.exists(lora_path):
            if get_cuda_free_memory_gb(gpu) < 2:
                return "❌ Low VRAM (<2GB). Free up memory.", None
            hf_hub_download(
                repo_id=repo_id,
                filename=filename,
                local_dir=LORA_CACHE,
                token=hf_token
            )
            with open(INSTALL_LOG, 'a') as f:
                f.write(f"[LoRA Download] {repo_id}/{filename} downloaded to {lora_path}\n")
        return "✅ LoRA downloaded", lora_path
    except Exception as e:
        with open(INSTALL_LOG, 'a') as f:
            f.write(f"[LoRA Download Error] {repo_id}/{filename}: {str(e)}\n")
        return f"❌ Download failed: {str(e)}", None

def load_lora(transformer, lora_path, lora_weight):
    try:
        if lora_path and os.path.exists(lora_path):
            if hasattr(transformer, 'load_lora_weights'):
                transformer.load_lora_weights(
                    lora_path,
                    adapter_name="fastvideo",
                    weight=lora_weight
                )
                with open(INSTALL_LOG, 'a') as f:
                    f.write(f"[LoRA Load] {lora_path} loaded with standard method, weight {lora_weight}\n")
                return "✅ LoRA loaded"
            else:
                # Manual LoRA loading
                lora_weights = load_file(lora_path)
                state_dict = transformer.state_dict()
                for key, value in lora_weights.items():
                    if key in state_dict:
                        state_dict[key] = state_dict[key] + lora_weight * value.to(state_dict[key].device)
                    else:
                        # Try partial key matching for common transformer layers
                        for model_key in state_dict:
                            if key.split('.')[-1] in model_key and ('self_attn' in model_key or 'ffn' in model_key):
                                state_dict[model_key] = state_dict[model_key] + lora_weight * value.to(state_dict[model_key].device)
                                break
                        else:
                            with open(INSTALL_LOG, 'a') as f:
                                f.write(f"[LoRA Load Warning] Key {key} not found in model state_dict\n")
                transformer.load_state_dict(state_dict)
                with open(INSTALL_LOG, 'a') as f:
                    f.write(f"[LoRA Load] {lora_path} loaded manually, weight {lora_weight}\n")
                return "✅ LoRA loaded manually"
        return "❌ No valid LoRA path"
    except Exception as e:
        with open(INSTALL_LOG, 'a') as f:
            f.write(f"[LoRA Load Error] {lora_path}: {str(e)}\n")
        return f"⚠️ LoRA not supported, using base model: {str(e)}"

def delete_lora(sel):
    try:
        loras = list_loras()
        if sel in [os.path.basename(p) for p in loras]:
            pth = loras[[os.path.basename(p) for p in loras].index(sel)]
            os.remove(pth)
            with open(INSTALL_LOG, 'a') as f:
                f.write(f"[LoRA Delete] {pth} deleted\n")
            return "✅ LoRA deleted", gr.update(choices=[os.path.basename(l) for l in list_loras()], value=None)
        return "❌ No LoRA selected", gr.update()
    except Exception as e:
        return f"❌ Delete failed: {str(e)}", gr.update()

# ---------------- Model load -----------------------------
free_mem = get_cuda_free_memory_gb(gpu)
hv       = free_mem > 60

try:
    text_encoder = LlamaModel.from_pretrained(
        "hunyuanvideo-community/HunyuanVideo",
        subfolder='text_encoder', torch_dtype=torch.float16, token=HF_TOKEN
    ).cpu().eval()
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] text_encoder: {str(e)}\n")
    raise gr.Error(f"Failed to load text_encoder: {str(e)}")

try:
    text_encoder_2 = CLIPTextModel.from_pretrained(
        "hunyuanvideo-community/HunyuanVideo",
        subfolder='text_encoder_2', torch_dtype=torch.float16, token=HF_TOKEN
    ).cpu().eval()
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] text_encoder_2: {str(e)}\n")
    raise gr.Error(f"Failed to load text_encoder_2: {str(e)}")

try:
    tokenizer = LlamaTokenizerFast.from_pretrained(
        "hunyuanvideo-community/HunyuanVideo",
        subfolder='tokenizer', token=HF_TOKEN
    )
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] tokenizer: {str(e)}\n")
    raise gr.Error(f"Failed to load tokenizer: {str(e)}")

try:
    tokenizer_2 = CLIPTokenizer.from_pretrained(
        "hunyuanvideo-community/HunyuanVideo",
        subfolder='tokenizer_2', token=HF_TOKEN
    )
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] tokenizer_2: {str(e)}\n")
    raise gr.Error(f"Failed to load tokenizer_2: {str(e)}")

try:
    vae = AutoencoderKLHunyuanVideo.from_pretrained(
        "hunyuanvideo-community/HunyuanVideo",
        subfolder='vae', torch_dtype=torch.float16, token=HF_TOKEN
    ).cpu().eval()
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] vae: {str(e)}\n")
    raise gr.Error(f"Failed to load vae: {str(e)}")

try:
    feature_extractor = SiglipImageProcessor.from_pretrained(
        "lllyasviel/flux_redux_bfl", subfolder='feature_extractor', token=HF_TOKEN
    )
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] feature_extractor: {str(e)}\n")
    raise gr.Error(f"Failed to load feature_extractor: {str(e)}")

try:
    image_encoder = SiglipVisionModel.from_pretrained(
        "lllyasviel/flux_redux_bfl",
        subfolder='image_encoder', torch_dtype=torch.float16, token=HF_TOKEN
    ).cpu().eval()
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] image_encoder: {str(e)}\n")
    raise gr.Error(f"Failed to load image_encoder: {str(e)}")

try:
    transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained(
        "lllyasviel/FramePack_F1_I2V_HY_20250503",
        torch_dtype=torch.bfloat16, token=HF_TOKEN
    ).cpu().eval()
except Exception as e:
    with open(INSTALL_LOG, 'a') as f:
        f.write(f"[Model Load Error] transformer: {str(e)}\n")
    raise gr.Error(f"Failed to load transformer: {str(e)}")

if not hv:
    vae.enable_slicing()
    vae.enable_tiling()

transformer.high_quality_fp32_output_for_inference = True
transformer.to(dtype=torch.bfloat16)

for m in (vae, image_encoder, text_encoder, text_encoder_2):
    m.to(dtype=torch.float16)
for m in (vae, image_encoder, text_encoder, text_encoder_2, transformer):
    m.requires_grad_(False)

if not hv:
    DynamicSwapInstaller.install_model(transformer, device=gpu)
    DynamicSwapInstaller.install_model(text_encoder, device=gpu)
else:
    for m in (text_encoder, text_encoder_2, image_encoder, vae, transformer):
        m.to(gpu)

stream = AsyncStream()

# ---------------- Worker -------------------------------
@torch.no_grad()
def worker(img, prompt, n_p, seed, secs, win, stp, cfg, gsc, rsc, keep, tea, crf, lora_path, lora_weight, disable_prompt_mods):
    # Download and load LoRA if specified
    lora_msg = "No LoRA specified"
    if lora_path:
        try:
            if lora_path.startswith("http") or lora_path.startswith("Kijai/"):
                repo_id = "Kijai/HunyuanVideo_comfy"
                filename = "hyvideo_FastVideo_LoRA-fp8.safetensors"
                lora_msg, lora_path = download_lora(repo_id, filename, HF_TOKEN)
                if not lora_path:
                    raise gr.Error(lora_msg)
            lora_msg = load_lora(transformer, lora_path, lora_weight)
            if "⚠️" in lora_msg or "❌" in lora_msg:
                print(lora_msg)
            else:
                stp = 8  # Override steps for FastVideo LoRA
        except Exception as e:
            with open(INSTALL_LOG, 'a') as f:
                f.write(f"[LoRA Error] {lora_path}: {str(e)}\n")
            lora_msg = f"⚠️ LoRA failed, using base model: {str(e)}"

    # Validate prompt
    try:
        if not disable_prompt_mods:
            if "stop" not in prompt.lower() and secs > 5:
                prompt += " The subject stops moving after 5 seconds."
            if "smooth" not in prompt.lower():
                prompt = f"Smooth animation: {prompt}"
            if "silent" not in prompt.lower():
                prompt += ", silent"
        if len(prompt.split()) > 50:
            print("Warning: Complex prompt may slow rendering or cause instability.")
    except Exception as e:
        raise gr.Error(f"Prompt validation failed: {str(e)}")

    # Check VRAM availability
    if get_cuda_free_memory_gb(gpu) < 2:
        raise gr.Error("Low VRAM (<2GB). Lower 'kee' or 'win'.")

    sections = max(round((secs*30)/(win*4)), 1)
    jid = generate_timestamp()
    try:
        with open(PROMPT_LOG, 'a') as f:
            f.write(f"{jid}\t{prompt}\t{n_p}\n")
    except Exception as e:
        print(f"Failed to log prompt: {str(e)}")

    stream.output_queue.push(('progress', (None, "", make_progress_bar_html(0, "Start"))))
    try:
        if not hv:
            unload_complete_models(text_encoder, text_encoder_2, image_encoder, vae, transformer)
            fake_diffusers_current_device(text_encoder, gpu)
            load_model_as_complete(text_encoder_2, gpu)
        lv, cp = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
        if cfg == 1:
            lv_n = torch.zeros_like(lv)
            cp_n = torch.zeros_like(cp)
        else:
            lv_n, cp_n = encode_prompt_conds(n_p, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
        lv, m  = crop_or_pad_yield_mask(lv, 512)
        lv_n, m_n = crop_or_pad_yield_mask(lv_n, 512)
        lv, cp, lv_n, cp_n = [x.to(torch.bfloat16) for x in (lv, cp, lv_n, cp_n)]
        H, W, _ = img.shape
        h, w    = find_nearest_bucket(H, W, 640)
        img_np  = resize_and_center_crop(img, w, h)
        Image.fromarray(img_np).save(os.path.join(OUT_IMG, f"{jid}.png"))
        img_pt  = (torch.from_numpy(img_np).float()/127.5-1).permute(2,0,1)[None,:,None]
        if not hv:
            load_model_as_complete(vae, gpu)
        start_lat = vae_encode(img_pt, vae)
        if not hv:
            load_model_as_complete(image_encoder, gpu)
        img_emb = hf_clip_vision_encode(img_np, feature_extractor, image_encoder).last_hidden_state.to(torch.bfloat16)
        gen = torch.Generator("cpu").manual_seed(seed)
        hist_lat = torch.zeros((1,16,1+2+16,h//8,w//8), dtype=torch.float16).cpu()
        hist_px = None
        total    = 0
        pad_seq = [3] + [2]*(sections-3) + [1,0] if sections>4 else list(reversed(range(sections)))
        for pad in pad_seq:
            last = pad == 0
            if stream.input_queue.top() == "end":
                stream.output_queue.push(("end", None))
                return
            pad_sz = pad * win
            idx = torch.arange(0, sum([1,pad_sz,win,1,2,16]))[None].to(device=gpu)
            a,b,c,d,e,f = idx.split([1,pad_sz,win,1,2,16],1)
            clean_idx = torch.cat([a,d],1)
            pre = start_lat.to(hist_lat)
            post, two, four = hist_lat[:,:,:1+2+16].split([1,2,16],2)
            clean = torch.cat([pre, post],2)
            if not hv:
                unload_complete_models()
                move_model_to_device_with_memory_preservation(transformer, gpu, keep)
            transformer.initialize_teacache(tea, stp)
            def cb(d):
                pv = vae_decode_fake(d["denoised"])
                pv = (pv*255).cpu().numpy().clip(0,255).astype(np.uint8)
                pv = einops.rearrange(pv, "b c t h w -> (b h) (t w) c")
                cur = d["i"]+1
                stream.output_queue.push(('progress', (pv, f"{cur}/{stp}", make_progress_bar_html(int(100*cur/stp), f"{cur}/{stp}"))))
                if stream.input_queue.top()=="end":
                    stream.output_queue.push(("end", None))
                    raise KeyboardInterrupt
            new_lat = sample_hunyuan(
                transformer=transformer, sampler="unipc", width=w, height=h, frames=win*4-3,
                real_guidance_scale=cfg, distilled_guidance_scale=gsc, guidance_rescale=rsc,
                num_inference_steps=stp, generator=gen,
                prompt_embeds=lv, prompt_embeds_mask=m, prompt_poolers=cp,
                negative_prompt_embeds=lv_n, negative_prompt_embeds_mask=m_n, negative_prompt_poolers=cp_n,
                device=gpu, dtype=torch.bfloat16, image_embeddings=img_emb,
                latent_indices=c, clean_latents=clean, clean_latent_indices=clean_idx,
                clean_latents_2x=two, clean_latent_2x_indices=e,
                clean_latents_4x=four, clean_latent_4x_indices=f, callback=cb
            )
            if last:
                new_lat = torch.cat([start_lat.to(new_lat), new_lat],2)
            total += new_lat.shape[2]
            hist_lat = torch.cat([new_lat.to(hist_lat), hist_lat],2)
            if not hv:
                offload_model_from_device_for_memory_preservation(transformer, gpu, 8)
                load_model_as_complete(vae, gpu)
            real = hist_lat[:,:,:total]
            if hist_px is None:
                hist_px = vae_decode(real, vae).cpu()
            else:
                overlap = win*4-3
                curr = vae_decode(real[:,:,:win*2], vae).cpu()
                hist_px = soft_append_bcthw(curr, hist_px, overlap)
            if not hv:
                unload_complete_models()
            tmp = os.path.join(OUT_TMP, f"{jid}_{total}.mp4")
            save_bcthw_as_mp4(hist_px, tmp, fps=30, crf=crf)
            stream.output_queue.push(('file', tmp))
            if last:
                fin = os.path.join(OUT_VID, f"{jid}_{total}.mp4")
                os.replace(tmp, fin)
                stream.output_queue.push(('complete', fin))
                break
    except Exception as e:
        traceback.print_exc()
        with open(INSTALL_LOG, 'a') as f:
            f.write(f"[Worker Error] {str(e)}\n")
        stream.output_queue.push(("end", None))
    return lora_msg

# ---------------- Process Function -----------------------
@torch.no_grad()
def process(img, prm, npr, sd, sec, win, stp, cfg, gsc, rsc, kee, tea, crf, lora_path, lora_weight, disable_prompt_mods):
    global stream
    if img is None:
        raise gr.Error("Upload an image")
    yield None, None, "", "", gr.update(interactive=False), gr.update(interactive=True), gr.update()
    stream = AsyncStream()
    lora_msg = async_run(worker, img, prm, npr, sd, sec, win, stp, cfg, gsc, rsc, kee, tea, crf, lora_path, lora_weight, disable_prompt_mods)
    out, log = None, ""
    while True:
        flag, data = stream.output_queue.next()
        if flag == "file":
            out = data
            yield out, gr.update(), gr.update(), log, gr.update(interactive=False), gr.update(interactive=True), gr.update(value=lora_msg)
        if flag == "progress":
            pv, desc, html = data
            log = desc
            yield gr.update(), gr.update(visible=True, value=pv), desc, html, gr.update(interactive=False), gr.update(interactive=True), gr.update(value=lora_msg)
        if flag in ("complete", "end"):
            yield out, gr.update(visible=False), gr.update(), "", gr.update(interactive=True), gr.update(interactive=False), gr.update(value=lora_msg)
            break

def end_process():
    stream.input_queue.push("end")

# ------------------- UI ------------------------------
quick_prompts = [
    ["Smooth animation: A character waves for 3 seconds, then stands still for 2 seconds, static camera, silent."],
    ["Smooth animation: A character moves for 5 seconds, static camera, silent."]
]
css = make_progress_bar_css() + """
.orange-button{background:#ff6200;color:#fff;border-color:#ff6200;}
.load-button{background:#4CAF50;color:#fff;border-color:#4CAF50;margin-left:10px;}
.big-setting-button{background:#0066cc;color:#fff;border:none;padding:14px 24px;font-size:18px;width:100%;border-radius:6px;margin:8px 0;}
.styled-dropdown{width:250px;padding:5px;border-radius:4px;}
.viewer-column{width:100%;max-width:900px;margin:0 auto;}
.media-preview img,.media-preview video{max-width:100%;height:380px;object-fit:contain;border:1px solid #444;border-radius:6px;}
.media-container{display:flex;gap:20px;align-items:flex-start;}
.control-box{min-width:220px;}
.control-grid{display:grid;grid-template-columns:1fr 1fr;gap:10px;}
.image-gallery{display:grid!important;grid-template-columns:repeat(auto-fit,minmax(300px,1fr))!important;gap:10px;padding:10px!important;overflow-y:auto!important;max-height:360px!important;}
.image-gallery .gallery-item{padding:10px;height:360px!important;width:300px!important;}
.image-gallery img{object-fit:contain;height:360px!important;width:300px!important;}
.video-gallery{display:grid!important;grid-template-columns:repeat(auto-fit,minmax(300px,1fr))!important;gap:10px;padding:10px!important;overflow-y:auto!important;max-height:360px!important;}
.video-gallery .gallery-item{padding:10px;height:360px!important;width:300px!important;}
.video-gallery video{object-fit:contain;height:360px!important;width:300px!important;}
.lora-gallery{display:grid!important;grid-template-columns:repeat(auto-fit,minmax(300px,1fr))!important;gap:10px;padding:10px!important;overflow-y:auto!important;max-height:360px!important;}
.lora-gallery .gallery-item{padding:10px;height:360px!important;width:300px!important;}
.lora-gallery .gallery-item div{text-align:center;font-size:16px;color:#fff;}
"""

blk = gr.Blocks(css=css).queue()
with blk:
    gr.Markdown("# 👻 GhostPack F1 Pro")
    with gr.Tabs():

        with gr.TabItem("👻 Generate"):
            with gr.Row():
                with gr.Column():
                    img_in = gr.Image(sources="upload", type="numpy", label="Image", height=320)
                    prm    = gr.Textbox(label="Prompt")
                    npr    = gr.Textbox(label="Negative Prompt", value="low quality, blurry, speaking, talking, moaning, vocalizing, lip movement, mouth animation, sound, dialogue, speech, whispering, shouting, lip sync, facial animation, expressive face, verbal expression, animated mouth")
                    save_msg = gr.Markdown("")
                    lora_path = gr.Textbox(
                        label="FastVideo LoRA Path or HF Repo",
                        value="Kijai/HunyuanVideo_comfy",
                        placeholder="e.g., Kijai/HunyuanVideo_comfy/hyvideo_FastVideo_LoRA-fp8.safetensors or /path/to/hyvideo_FastVideo_LoRA-fp8.safetensors"
                    )
                    lora_weight = gr.Slider(label="LoRA Weight", minimum=0.5, maximum=1.5, value=1.0, step=0.1)
                    disable_prompt_mods = gr.Checkbox(label="Disable Prompt Modifications", value=False)
                    lora_status_gen = gr.Markdown(value=auto_download_status)
                    btn_save = gr.Button("Save Prompt")
                    btn1, btn2, btn3 = gr.Button("Load Most Recent"), gr.Button("Load 2nd Recent"), gr.Button("Load 3rd Recent")
                    ds = gr.Dataset(samples=quick_prompts, label="Quick List", components=[prm])
                    ds.click(lambda x: x[0], [ds], [prm])
                    btn_save.click(save_prompt_fn, [prm, npr], [save_msg])
                    btn1.click(lambda: load_prompt_fn(0), [], [prm])
                    btn2.click(lambda: load_prompt_fn(1), [], [prm])
                    btn3.click(lambda: load_prompt_fn(2), [], [prm])
                    with gr.Row():
                        b_go, b_end = gr.Button("Start"), gr.Button("End", interactive=False)
                    with gr.Group():
                        tea = gr.Checkbox(label="Use TeaCache", value=True)
                        se  = gr.Number(label="Seed", value=31337, precision=0)
                        sec = gr.Slider(label="Video Length (s)", minimum=1, maximum=120, value=5, step=0.1)
                        win = gr.Slider(label="Latent Window", minimum=1, maximum=33, value=5, step=1)
                        stp = gr.Slider(label="Steps", minimum=1, maximum=100, value=8, step=1)
                        cfg = gr.Slider(label="CFG", minimum=1, maximum=32, value=1, step=0.01, visible=False)
                        gsc = gr.Slider(label="Distilled CFG", minimum=1, maximum=32, value=5, step=0.01)
                        rsc = gr.Slider(label="CFG Re-Scale", minimum=0, maximum=1, value=0.5, step=0.01)
                        kee = gr.Slider(label="GPU Keep (GB)", minimum=4, maximum=free_mem, value=6, step=0.1)
                        crf = gr.Slider(label="MP4 CRF", minimum=0, maximum=100, value=20, step=1)
                with gr.Column():
                    pv  = gr.Image(label="Next Latents", height=200, visible=False)
                    vid = gr.Video(label="Finished", autoplay=True, height=500, loop=True, show_share_button=False)
                    log_md = gr.Markdown("")
                    bar    = gr.HTML("")
                b_go.click(
                    process,
                    [img_in, prm, npr, se, sec, win, stp, cfg, gsc, rsc, kee, tea, crf, lora_path, lora_weight, disable_prompt_mods],
                    [vid, pv, log_md, bar, b_go, b_end, lora_status_gen]
                )
                b_end.click(end_process)

        with gr.TabItem("🖼️ Image Gallery"):
            with gr.Row(elem_classes="media-container"):
                with gr.Column(scale=3):
                    image_preview = gr.Image(
                        label="Viewer",
                        value=(list_images()[0] if list_images() else None),
                        interactive=False, elem_classes="media-preview"
                    )
                with gr.Column(elem_classes="control-box"):
                    image_dropdown = gr.Dropdown(
                        choices=[os.path.basename(i) for i in list_images()],
                        value=(os.path.basename(list_images()[0]) if list_images() else None),
                        label="Select", elem_classes="styled-dropdown"
                    )
                    with gr.Row(elem_classes="control-grid"):
                        load_btn = gr.Button("Load", elem_classes="load-button")
                        next_btn = gr.Button("Next", elem_classes="load-button")
                    with gr.Row(elem_classes="control-grid"):
                        refresh_btn = gr.Button("Refresh")
                        delete_btn  = gr.Button("Delete", elem_classes="orange-button")
            image_gallery = gr.Gallery(
                value=list_images(), label="Thumbnails", columns=6, height=360,
                allow_preview=False, type="filepath", elem_classes="image-gallery"
            )
            load_btn.click(load_image, [image_dropdown], [image_preview, image_dropdown])
            next_btn.click(next_image_and_load, [image_dropdown], [image_preview, image_dropdown])
            refresh_btn.click(lambda: (
                gr.update(choices=[os.path.basename(i) for i in list_images()],
                          value=os.path.basename(list_images()[0]) if list_images() else None),
                gr.update(value=list_images()[0] if list_images() else None),
                gr.update(value=list_images())
            ), [], [image_dropdown, image_preview, image_gallery])
            delete_btn.click(lambda sel: (os.remove(os.path.join(OUT_IMG, sel)) if sel else None) or load_image(""),
                             [image_dropdown], [image_preview, image_dropdown])
            image_gallery.select(gallery_image_select, [], [image_preview, image_dropdown])

        with gr.TabItem("🎬 Video Gallery"):
            with gr.Row(elem_classes="media-container"):
                with gr.Column(scale=3):
                    video_preview = gr.Video(
                        label="Viewer",
                        value=(list_videos()[0] if list_videos() else None),
                        autoplay=True, loop=True, interactive=False, elem_classes="media-preview"
                    )
                with gr.Column(elem_classes="control-box"):
                    video_dropdown = gr.Dropdown(
                        choices=[os.path.basename(v) for v in list_videos()],
                        value=(os.path.basename(list_videos()[0]) if list_videos() else None),
                        label="Select", elem_classes="styled-dropdown"
                    )
                    with gr.Row(elem_classes="control-grid"):
                        load_vbtn = gr.Button("Load", elem_classes="load-button")
                        next_vbtn = gr.Button("Next", elem_classes="load-button")
                    with gr.Row(elem_classes="control-grid"):
                        refresh_v = gr.Button("Refresh")
                        delete_v  = gr.Button("Delete", elem_classes="orange-button")
            video_gallery = gr.Gallery(
                value=list_videos(), label="Thumbnails", columns=6, height=360,
                allow_preview=False, type="filepath", elem_classes="video-gallery"
            )
            load_vbtn.click(load_video, [video_dropdown], [video_preview, video_dropdown])
            next_vbtn.click(next_video_and_load, [video_dropdown], [video_preview, video_dropdown])
            refresh_v.click(lambda: (
                gr.update(choices=[os.path.basename(v) for v in list_videos()],
                          value=os.path.basename(list_videos()[0]) if list_videos() else None),
                gr.update(value=list_videos()[0] if list_videos() else None),
                gr.update(value=list_videos())
            ), [], [video_dropdown, video_preview, video_gallery])
            delete_v.click(lambda sel: (os.remove(os.path.join(OUT_VID, sel)) if sel else None) or load_video(""),
                            [video_dropdown], [video_preview, video_dropdown])
            video_gallery.select(gallery_video_select, [], [video_preview, video_dropdown])

        with gr.TabItem("📦 LoRA Management"):
            with gr.Row(elem_classes="media-container"):
                with gr.Column(scale=3):
                    lora_status = gr.Markdown("")
                with gr.Column(elem_classes="control-box"):
                    lora_dropdown = gr.Dropdown(
                        choices=[os.path.basename(l) for l in list_loras()],
                        value=(os.path.basename(list_loras()[0]) if list_loras() else None),
                        label="Select LoRA", elem_classes="styled-dropdown"
                    )
                    with gr.Row(elem_classes="control-grid"):
                        load_lora_btn = gr.Button("Load", elem_classes="load-button")
                        next_lora_btn = gr.Button("Next", elem_classes="load-button")
                    with gr.Row(elem_classes="control-grid"):
                        refresh_lora_btn = gr.Button("Refresh")
                        delete_lora_btn = gr.Button("Delete", elem_classes="orange-button")
                    download_fastvideo_btn = gr.Button("Download FastVideo LoRA", elem_classes="big-setting-button")
            lora_gallery = gr.Gallery(
                value=[(l, os.path.basename(l)) for l in list_loras()], label="LoRA Files", columns=6, height=360,
                allow_preview=False, elem_classes="lora-gallery"
            )
            load_lora_btn.click(load_lora_select, [lora_dropdown], [lora_path, lora_dropdown])
            next_lora_btn.click(next_lora_and_load, [lora_dropdown], [lora_path, lora_dropdown])
            refresh_lora_btn.click(lambda: (
                gr.update(choices=[os.path.basename(l) for l in list_loras()],
                          value=os.path.basename(list_loras()[0]) if list_loras() else None),
                gr.update(value=[(l, os.path.basename(l)) for l in list_loras()])
            ), [], [lora_dropdown, lora_gallery])
            delete_lora_btn.click(delete_lora, [lora_dropdown], [lora_status, lora_dropdown])
            download_fastvideo_btn.click(
                lambda: auto_download_fastvideo_lora(),
                [], [lora_status]
            )
            lora_gallery.select(gallery_lora_select, [], [lora_path, lora_dropdown])

        with gr.TabItem("👻 About"):
            gr.Markdown("## GhostPack F1 Pro")
            with gr.Row():
                with gr.Column():
                    gr.Markdown("**🛠️ Description**\nImage-to-Video toolkit powered by HunyuanVideo & FramePack-F1")
                with gr.Column():
                    gr.Markdown("**📦 Version**\n2025-05-03")
                with gr.Column():
                    gr.Markdown("**✍️ Author**\nGhostAI")
                with gr.Column():
                    gr.Markdown("**🔗 Repo**\nhttps://huggingface.co/spaces/ghostai1/GhostPack")

        with gr.TabItem("⚙️ Settings"):
            ct = gr.Button("Clear Temp", elem_classes="big-setting-button")
            ctmsg = gr.Markdown("")
            co = gr.Button("Clear Old", elem_classes="big-setting-button")
            comsg= gr.Markdown("")
            ci = gr.Button("Clear Images", elem_classes="big-setting-button")
            cimg= gr.Markdown("")
            cv = gr.Button("Clear Videos", elem_classes="big-setting-button")
            cvid= gr.Markdown("")
            ct.click(clear_temp_videos, [], ctmsg)
            co.click(clear_old_files,   [], comsg)
            ci.click(clear_images,      [], cimg)
            cv.click(clear_videos,      [], cvid)

        with gr.TabItem("🛠️ Install"):
            xs = gr.Textbox(value=status_xformers(), interactive=False, label="xformers")
            bx = gr.Button("Install xformers", elem_classes="big-setting-button")
            ss = gr.Textbox(value=status_sage(), interactive=False, label="sage-attn")
            bs = gr.Button("Install sage-attn", elem_classes="big-setting-button")
            fs = gr.Textbox(value=status_flash(),interactive=False, label="flash-attn")
            bf = gr.Button("Install flash-attn", elem_classes="big-setting-button")
            bx.click(install_xformers,   [], xs)
            bs.click(install_sage_attn,  [], ss)
            bf.click(install_flash_attn, [], fs)

        with gr.TabItem("📜 Logs"):
            logs = gr.Textbox(lines=20, interactive=False, label="Install Logs")
            rl   = gr.Button("Refresh", elem_classes="big-setting-button")
            cl   = gr.Button("Clear",   elem_classes="big-setting-button")
            rl.click(refresh_logs, [], logs)
            cl.click(clear_logs,   [], logs)

    # Force video previews to seek to 2s
    gr.HTML("""<script>
document.querySelectorAll('.video-gallery video').forEach(v => {
  v.addEventListener('loadedmetadata', () => {
    if (v.duration > 2) v.currentTime = 2;
  });
});
</script>""")

blk.launch(
    server_name=args.server,
    server_port=args.port,
    share=args.share,
    inbrowser=args.inbrowser
)